Discover Awesome MCP Servers

AVS Document Search System

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

Monad NFT Launch Tool

An MCP server that helps users create NFT collections, deploy smart contracts to the Monad blockchain, and generate mint websites with Claude AI integration.

MCP Chat

A command-line interface application that enables interaction with LLMs through document retrieval, command-based prompts, and extensible tool integrations using the Model Control Protocol architecture.

Azure AI Foundry MCP Server

Enables interaction with Azure AI Foundry services through a unified interface for model exploration and deployment, knowledge indexing and search, AI evaluation, and fine-tuning operations. Supports both GitHub token-based model testing and full Azure deployment workflows.

Sentry MCP Server

Servidor de Protocolo de Contexto del Modelo (MCP) para Sentry

Directory Explorer MCP Server

A Model Context Protocol server that provides token-aware directory exploration and file analysis for Large Language Models, enabling intelligent codebase navigation with features like directory scanning, file content extraction, and token usage statistics.

Image Generator

I can't directly generate and return an image using Together.ai (or any other service) within this text-based environment. I am a language model, not an image generation tool. However, I can provide you with the information you need to do it yourself. Here's what you'd typically need to do: 1. **Access Together.ai's API:** You'll need to sign up for an account on Together.ai and obtain an API key. This key is how you authenticate your requests. 2. **Use their API documentation:** Together.ai will have documentation on how to use their image generation API. This documentation will tell you: * **The endpoint (URL) to send your request to.** * **The required parameters:** This will almost certainly include a text prompt (the description of the image you want to generate). It might also include parameters for image size, style, number of images, etc. * **The format of the request (usually JSON).** * **The format of the response:** This will likely include a URL or data representing the generated image. 3. **Write code to make the API request:** You'll need to use a programming language (like Python) and a library that can make HTTP requests (like `requests` in Python) to send the request to Together.ai's API. 4. **Process the response:** Once you get the response from Together.ai, you'll need to parse it to extract the image data or URL. 5. **Display or save the image:** Finally, you can display the image in your application or save it to a file. **Example (Conceptual Python Code - Requires Together.ai API Key and Installation of `requests`):** ```python import requests import json # Replace with your actual Together.ai API key API_KEY = "YOUR_TOGETHERAI_API_KEY" def generate_image(prompt): """Generates an image using Together.ai based on the given prompt.""" url = "THE_TOGETHERAI_IMAGE_GENERATION_ENDPOINT" # Replace with the actual endpoint headers = { "Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json" } data = { "prompt": prompt, "width": 512, # Example: Image width "height": 512 # Example: Image height # Add other parameters as needed based on Together.ai's documentation } try: response = requests.post(url, headers=headers, data=json.dumps(data)) response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx) response_json = response.json() # Assuming the response contains a URL to the image image_url = response_json.get("image_url") # Adjust based on the actual response structure if image_url: print(f"Image URL: {image_url}") # You can then download the image using requests.get(image_url) # and save it to a file. Or display it in a GUI. return image_url # Or return the image data itself if that's what the API provides else: print("Error: Image URL not found in the response.") return None except requests.exceptions.RequestException as e: print(f"Error making API request: {e}") return None except json.JSONDecodeError: print("Error: Could not decode JSON response.") return None # Example usage: prompt = "A futuristic cityscape at sunset" image_url = generate_image(prompt) if image_url: print("Image generated successfully!") # Further processing of the image (download, display, etc.) else: print("Image generation failed.") ``` **Important Considerations:** * **API Documentation is Key:** The most important thing is to carefully read and understand Together.ai's API documentation. The code above is just a general example; you'll need to adapt it to their specific requirements. * **Error Handling:** The example includes basic error handling, but you should add more robust error handling to catch potential issues like network problems, invalid API keys, or incorrect parameters. * **Rate Limits:** Be aware of Together.ai's rate limits (how many requests you can make per minute/hour). You might need to implement logic to handle rate limiting. * **Cost:** Using image generation APIs often incurs costs. Understand Together.ai's pricing model before you start using the API extensively. **Translation to Spanish (of the explanation, not the code):** No puedo generar y devolver directamente una imagen usando Together.ai (o cualquier otro servicio) dentro de este entorno basado en texto. Soy un modelo de lenguaje, no una herramienta de generación de imágenes. Sin embargo, puedo proporcionarte la información que necesitas para hacerlo tú mismo. Esto es lo que normalmente necesitarías hacer: 1. **Acceder a la API de Together.ai:** Necesitarás registrarte para obtener una cuenta en Together.ai y obtener una clave API. Esta clave es cómo autenticas tus solicitudes. 2. **Usar su documentación de la API:** Together.ai tendrá documentación sobre cómo usar su API de generación de imágenes. Esta documentación te dirá: * **El endpoint (URL) al que enviar tu solicitud.** * **Los parámetros requeridos:** Esto casi seguro que incluirá un prompt de texto (la descripción de la imagen que quieres generar). También podría incluir parámetros para el tamaño de la imagen, el estilo, el número de imágenes, etc. * **El formato de la solicitud (normalmente JSON).** * **El formato de la respuesta:** Esto probablemente incluirá una URL o datos que representen la imagen generada. 3. **Escribir código para hacer la solicitud a la API:** Necesitarás usar un lenguaje de programación (como Python) y una biblioteca que pueda hacer solicitudes HTTP (como `requests` en Python) para enviar la solicitud a la API de Together.ai. 4. **Procesar la respuesta:** Una vez que obtengas la respuesta de Together.ai, necesitarás analizarla para extraer los datos de la imagen o la URL. 5. **Mostrar o guardar la imagen:** Finalmente, puedes mostrar la imagen en tu aplicación o guardarla en un archivo. **Consideraciones importantes:** * **La documentación de la API es clave:** Lo más importante es leer y comprender cuidadosamente la documentación de la API de Together.ai. El código anterior es solo un ejemplo general; necesitarás adaptarlo a sus requisitos específicos. * **Manejo de errores:** El ejemplo incluye un manejo básico de errores, pero debes agregar un manejo de errores más robusto para detectar posibles problemas como problemas de red, claves API no válidas o parámetros incorrectos. * **Límites de velocidad:** Ten en cuenta los límites de velocidad de Together.ai (cuántas solicitudes puedes hacer por minuto/hora). Es posible que debas implementar lógica para manejar la limitación de velocidad. * **Costo:** El uso de las API de generación de imágenes a menudo incurre en costos. Comprende el modelo de precios de Together.ai antes de comenzar a usar la API extensivamente.

FoundryVTT MCP Server

Integrates with FoundryVTT tabletop gaming sessions, allowing AI assistants to query game data, roll dice, generate content (NPCs, loot, encounters), manage combat, and provide tactical suggestions through natural language.

Postgres MCP Pro

An open-source MCP server that provides AI agents with advanced PostgreSQL capabilities including index tuning, query plan optimization, and comprehensive database health analysis. It supports safe SQL execution through configurable access modes and offers both stdio and SSE transport options for various development environments.

nova-act-mcp

Un servidor MCP que proporciona herramientas para controlar navegadores web utilizando el SDK Amazon Nova Act. Permite flujos de trabajo de automatización de navegadores de varios pasos a través de agentes MCP.

RAGBrain MCP

Connects Claude Desktop to a RAGBrain knowledge base to enable semantic search, document retrieval, and namespace management. It allows users to browse collections, discover documents by topic, and access full text content through natural language.

OpenProject MCP Server

Enables integration with OpenProject for project management tasks including work package operations, time tracking, comments, and accessing reference data like statuses, types, and users through n8n workflows.

DateTime MCP Server

Provides LLMs with current date and time information across any timezone, with configurable defaults and support for IANA timezone identifiers.

Ant Design MCP Server

Provides access to Ant Design component library documentation and information for automated code generation. Enables searching components by category or keyword, retrieving component props, examples, API documentation, and available icons.

TurboPentest

MCP server for TurboPentest — run AI-powered penetration tests and review findings from your coding assistant.

MCP-IQWiki

A Model Context Protocol server that allows AI assistants and applications to access IQ.wiki data, enabling retrieval of specific wikis, user-created wikis, user-edited wikis, and detailed wiki activities.

EyeLevel RAG MCP Server

A local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.

Sample MCP Server

AI assistant application that integrates FastMCP server with MongoDB Atlas knowledge base, enabling direct MCP tool calling for document search and retrieval through a complete REST API.

Weather MCP Server

Enables real-time weather queries for 12 major Chinese cities and global locations using the wttr.in API. Built on the HelloAgents framework with no API key required.

BigQuery Validator

Enables validation and dry-run analysis of BigQuery SQL queries without execution. Provides cost estimates, schema previews, and syntax validation for BigQuery queries.

RAGStack-Lambda

Serverless document and media processing with AI chat. Upload documents, images, video, and audio — extract text with OCR or transcription — query using Amazon Bedrock.

OpenSIPS MCP Server

AI-powered control plane for OpenSIPS SIP servers, enabling natural-language operations and CLI-based management for configuration, migration, monitoring, and diagnostics.

Trackings MCP Server

Integrates Claude Desktop with trackings.ai to manage projects, configure keyword scans, and trigger run executions. It allows users to retrieve consolidated keyword results and monitor credit balances through natural language commands.

CallRail MCP

CallRail REST API v3 integration with 49 tools — calls, form submissions, transcripts, full CRUD on tags/trackers/companies/users/notifications, plus agency-specific aggregation tools (usage_summary, compare_periods, bulk_update_calls, spam_detector, call_eligibility_check).

Backlinks MCP

A MCP server for retrieving backlink information for any domain(SEO).

GitHub MCP Server

Un servidor MCP que permite a Claude y otros LLMs compatibles con MCP interactuar con la API de GitHub, admitiendo funciones como crear incidencias, obtener información del repositorio, listar incidencias y buscar repositorios.

ChatterBox MCP Server

A Model Context Protocol server that enables AI agents to join and interact with online meetings (Zoom and Google Meet), capturing transcripts and recordings to generate meeting summaries.

regulationsgov-mcp

Enables interaction with the Regulations.gov API to search federal rulemaking dockets, proposed and final rules, public comments, and comment periods. Supports tracking FAR/DFARS case histories and monitoring open comment periods across federal agencies with optional API key authentication for higher rate limits.

Cloudinary MCP Server

An MCP server that provides an interface for interacting with the Cloudinary API to manage media assets and cloud configurations. It enables secure resource management through API keys and OAuth2 authentication within MCP-compliant environments.